statsmodels.discrete.conditional_models.ConditionalResults¶

class statsmodels.discrete.conditional_models.ConditionalResults(model, params, normalized_cov_params, scale)[source]
Attributes:
bse

The standard errors of the parameter estimates.

llf

Log-likelihood of model

pvalues

The two-tailed p values for the t-stats of the params.

tvalues

Return the t-statistic for a given parameter estimate.

`use_t`

Flag indicating to use the Student’s distribution in inference.

Methods

 `conf_int`([alpha, cols]) Construct confidence interval for the fitted parameters. `cov_params`([r_matrix, column, scale, cov_p, ...]) Compute the variance/covariance matrix. `f_test`(r_matrix[, cov_p, invcov]) Compute the F-test for a joint linear hypothesis. `initialize`(model, params, **kwargs) Initialize (possibly re-initialize) a Results instance. `load`(fname) Load a pickled results instance See specific model class docstring `predict`([exog, transform]) Call self.model.predict with self.params as the first argument. Remove data arrays, all nobs arrays from result and model. `save`(fname[, remove_data]) Save a pickle of this instance. `summary`([yname, xname, title, alpha]) Summarize the fitted model. `t_test`(r_matrix[, cov_p, use_t]) Compute a t-test for a each linear hypothesis of the form Rb = q. `t_test_pairwise`(term_name[, method, alpha, ...]) Perform pairwise t_test with multiple testing corrected p-values. `wald_test`(r_matrix[, cov_p, invcov, use_f, ...]) Compute a Wald-test for a joint linear hypothesis. `wald_test_terms`([skip_single, ...]) Compute a sequence of Wald tests for terms over multiple columns.

Properties

 `bse` The standard errors of the parameter estimates. `llf` Log-likelihood of model `pvalues` The two-tailed p values for the t-stats of the params. `tvalues` Return the t-statistic for a given parameter estimate. `use_t` Flag indicating to use the Student's distribution in inference.

Last update: Sep 01, 2023